Flexible inverse adaptive fuzzy inference model to identify the evolution of operational value at risk for improving operational risk management

Abstract Operational risk was one of the most important risks in the 2008 global financial crisis. This is due to limitations of the applied models in explaining and estimating this type of risk from highly qualitative information related to failures in the operations of financial organizations. A review of research literature on this area indicates an increase in the development of models for the estimation of the operational value at risk. However, there is a lack of models that use qualitative information for estimating this type of risk. Motivated by this finding, we propose a Flexible Inverse Adaptive Fuzzy Inference Model that integrates both a novel Montecarlo sampling method for the linguistic input variables of frequency and severity that allow the characterization of a risk event, the impact of risk management matrices to estimate the loss distribution and the associated operational value at risk. The methodology follows a loss distribution approach as defined by Basel II. A benefit of the proposed model is that it works with highly qualitative risk data and it also connects the risk measurement (operational value at risk) with risk management, based on risk management matrices. This way, we mitigate limitations related to a lack of available operational risk event data when assessing operational risk. We evaluate the experimental results obtained through the proposed model by using the Index of Agreement indicator. The results provide a flexible loss distribution under different risk profiles or risk management matrices that explain the evolution of operational risk in real time.